Differential Privacy papers, 2023–2026

Key findings

• Differential Privacy fell from 117 papers in 2023 to 103 in 2026, so interest is cooling.
• NeurIPS publishes the most Differential Privacy work: 217 papers from 2023 to 2026, 45% of the four-venue total.
• The fastest-growing overlap is with Social aspects · accountability transparency and interpretability: papers tagged with both rose from 0 in 2023 to 22 in 2026.
• Among the 5 topics under Federated and Privacy, it ranks #3 by 2026 paper count; the fastest grower in the group, Secure Aggregation, is up 1.8x versus 0.9x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026A Unified Framework for Adversary-Aware Differential Privacy Bounds
• ICML 2026Optimal conversion from Rényi Differential Privacy to $f$-Differential Privacy
• ICLR 2026Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models
• CVPR 2026Fully Decentralized Certified Unlearning
• NeurIPS 2025Continual Release Moment Estimation with Differential Privacy
• ICML 2025Leveraging Per-Instance Privacy for Machine Unlearning
• ICLR 2025Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
• CVPR 2025Focal Split: Untethered Snapshot Depth from Differential Defocus
• NeurIPS 2024$\texttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning
• ICML 2024Delving into Differentially Private Transformer
• ICLR 2024Decoupled Marked Temporal Point Process using Neural Ordinary Differential Equations
• CVPR 2024Affine Equivariant Networks Based on Differential Invariants
• NeurIPS 2023(Amplified) Banded Matrix Factorization: A unified approach to private training
• ICML 2023Bayesian Estimation of Differential Privacy
• ICLR 2023A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy
• CVPR 2023Learning To Generate Image Embeddings With User-Level Differential Privacy
• NeurIPS 2026Asymptotically Optimal Best Arm Identification with Fixed-Budget under Differential Privacy
• ICML 2026Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov–Arnold Networks
• ICLR 2026Beyond Membership: Limitations of Add/Remove Adjacency in Differential Privacy
• CVPR 2026LDP-Slicing Local Differential Privacy for Images via Randomized Bit-Plane Slicing

All Differential Privacy papers

103 papers, page 2 of 3

51. ICML 2026Differentially Private Synthetic Data via APIs 4: Tabular Data
52. ICML 2026DP-KFC: Data-Free Preconditioning for Privacy-Preserving Deep Learning
53. ICML 2026Efficient DP-SGD for LLMs with Randomized Clipping
54. ICML 2026Efficient Public Verification of Private ML via Regularization
55. ICML 2026FIBER: A Differentially Private Optimizer with Filter-Aware Innovation Bias Correction
56. ICML 2026Finding Differentially Private Second Order Stationary Points in Stochastic Minimax Optimization
57. ICML 2026Keeping a Secret Requires a Good Memory: Unconditional Streaming Lower-Bounds for Differentially Private Algorithms
58. ICML 2026LAPRAS : Learning-Augmented PRivate Answering for linear query Streams.
59. ICML 2026Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy
60. ICML 2026Near-Optimal Private Linear Regression via Iterative Hessian Mixing
61. ICML 2026OptiFluence: Principled Design of Privacy Canaries
62. ICML 2026Optimal conversion from Rényi Differential Privacy to $f$-Differential Privacy
63. ICML 2026Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation
64. ICML 2026Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov–Arnold Networks
65. ICML 2026Persuasive Privacy
66. ICML 2026PISA: Privacy-Preserving Split Adaptation with Model IP Protection
67. ICML 2026PRISM: Gauge-Invariant Tangent-Space Differentially Private LoRA
68. ICML 2026Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States
69. ICML 2026Privacy-Aware Data Integration for Enhanced Quantile Inference under Heterogeneity
70. ICML 2026Private and Stable Test-time Adaptation with Differential Privacy
71. ICML 2026Private Learning with Public Feature Conditioning
72. ICML 2026Privately Fine-Tuned LLMs Preserve Temporal Dynamics in Tabular Data
73. ICML 2026PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees
74. ICML 2026Regret-Based Federated Causal Discovery with Unknown Interventions
75. ICML 2026Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
76. ICML 2026scDEBART: Predicting in silico Single-Cell Perturbation Responses via Large-Scale Differential Expression Learning
77. ICML 2026Shuffling-Aware Optimization for Private Vector Mean Estimation
78. ICML 2026SlaClip: Gradient Norm Slacks can be Indicator for Adaptive Clipping in DP-SGD
79. ICML 2026Solving Positive Linear Programs with Differential Privacy
80. ICML 2026Understanding Private Learning From Feature Perspective
81. NeurIPS 2026A Private Empirical Defense Against Privacy Audits
82. NeurIPS 2026A Unified Framework for Adversary-Aware Differential Privacy Bounds
83. NeurIPS 2026Aegis: Generative Gradient Masking for Privacy-Preserving Medical Federated Learning
84. NeurIPS 2026Asymptotically Optimal Best Arm Identification with Fixed-Budget under Differential Privacy
85. NeurIPS 2026Benchmarking Membership Privacy Risks in Preference-Based LLM Post-Training
86. NeurIPS 2026Breakeven complexity: A new perspective on neural partial differential equation solvers
87. NeurIPS 2026Differential Item Functioning as an Item-Level Diagnostic for LLM Benchmarks
88. NeurIPS 2026Differential Vector Erasure: Unified Training-Free Concept Erasure for Flow Matching Models
89. NeurIPS 2026DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy
90. NeurIPS 2026𝑓-Differential Privacy Filters: Validity and Approximate Solutions
91. NeurIPS 2026Improved Regret bounds in Tabular Reinforcement Learning under Local Differential Privacy
92. NeurIPS 2026Lie Generator Networks for Nonlinear Partial Differential Equations
93. NeurIPS 2026Neural Fractional Stochastic Differential Equations
94. NeurIPS 2026Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
95. NeurIPS 2026On Differential Private $\ell_1$, $\ell_2$ and $\ell_p^p$ Distance Queries
96. NeurIPS 2026One-Shot Private Confidence Regions via Resampling
97. NeurIPS 2026Optimal Algorithms for Fixed-Graph Multi-Attribution Privacy
98. NeurIPS 2026Privacy-Preserving Retrieval-Augmented Generation with Plausible Deniability
99. NeurIPS 2026Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy
100. NeurIPS 2026Random Neural Network Expressivity for Non-Linear Partial Differential Equations

FAQ

How many Differential Privacy papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

479 in total: 117 in 2023, 113 in 2024, 146 in 2025, 103 in 2026.

How many Differential Privacy papers are at NeurIPS 2026?

NeurIPS 2026 has 23 Differential Privacy papers, versus 66 the year before.

How many Differential Privacy papers are at ICLR 2026?

ICLR 2026 has 31 Differential Privacy papers, versus 29 the year before.

How many Differential Privacy papers are at ICML 2026?

ICML 2026 has 42 Differential Privacy papers, versus 47 the year before.

How many Differential Privacy papers are at CVPR 2026?

CVPR 2026 has 7 Differential Privacy papers, versus 4 the year before.

Which conference publishes the most Differential Privacy papers?

NeurIPS, with 217 papers from 2023 to 2026.